Radioligands for Tropomyosin Receptor Kinase (Trk) Positron Emission Tomography Imaging
Bibliographic record
Abstract
The tropomyosin receptor kinases family (TrkA, TrkB, and TrkC) supports neuronal growth, survival, and differentiation during development, adult life, and aging. TrkA/B/C downregulation is a prominent hallmark of various neurological disorders including Alzheimer's disease (AD). Abnormally expressed or overexpressed full-length or oncogenic fusion TrkA/B/C proteins were shown to drive tumorigenesis in a variety of neurogenic and non-neurogenic human cancers and are currently the focus of intensive clinical research. Neurologic and oncologic studies of the spatiotemporal alterations in TrkA/B/C expression and density and the determination of target engagement of emerging antineoplastic clinical inhibitors in normal and diseased tissue are crucially needed but have remained largely unexplored due to the lack of suitable non-invasive probes. Here, we review the recent development of carbon-11- and fluorine-18-labeled positron emission tomography (PET) radioligands based on specifically designed small molecule kinase catalytic domain-binding inhibitors of TrkA/B/C. Basic developments in medicinal chemistry, radiolabeling and translational PET imaging in multiple species including humans are highlighted.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".